How to get control over the sample size?

[LABclimate]

Hello,

I would like to use bootstrap to calculate statistical significance of mean values of composites.
The composites have length l and are taken from a time-series of length L > l. Now I would like to compute null-composites by randomly selecting the same number of dates (i.e.: l elements) from the whole time-series as had been in the original composite.

Playing around I ended up with:
from arch.bootstrap import MovingBlockBootstrap
x = numpy.arange(1000) (original dataset)
MovingBlockBootstrap(3,x).conf_int(numpy.mean, reps=10000, size=.95)

However, MovingBlockBootstraps leaves me with composites of length l = L = 1000. Is there a way to change this?

I wondered, whether a function like
import random
def func(x): return np.mean(random.sample(x,l))
could do the job. But testing this with l=200, I got quite high fluctuations for the confidence intervals...

I would be very happy about any hint!
Thank you very much!
L

PS: I'm not sure, whether this github page is the right place to ask that kind of questions. If not I am happily willing to migrate the question to any other forum.

[bashtage]

The bootstrap sample is the same size as the original one. If you want. Smaller sample, you could do something like

k=20
def somestat(x):
    return stat(x[:k])

This is inefficient but simple.

[bashtage]

Let me know if this works for you.